Blockchain (BC) and smart contracts (SCs) offer strong potential to enhance transparency, coordination, and trust in construction projects, a sector critical to global economic growth yet slow in adopting digital technologies. However, adoption remains limited due to persistent security vulnerabilities (SVs), including coding flaws, governance weaknesses, and cyberattacks, which undermine reliability. Existing studies often rely on traditional decision-making approaches, which overlook the complex interrelationships among these vulnerabilities. This study aims to systematically identify, classify, and prioritize SC vulnerabilities in BC-enabled construction projects using an integrated decision framework. A four-stage methodology was employed: (1) expert consultation and literature review identified 18 SVs; (2) decision-making trial and evaluation laboratory (DEMATEL) established cause-and-effect relationships; (3) machine learning (ML) validated the results with 88.9% accuracy; and (4) interpretive structural modeling (ISM) and cross-impact matrix multiplication applied to classification (MICMAC) analysis prioritized vulnerabilities and revealed their hierarchical structure. The results show that code bugs and flaws (SV1), governance vulnerabilities (SV17), race conditions (SV18), and Sybil attacks (SV7) are the most critical SVs, with sensitivity analysis confirming robustness. The proposed framework advances theory by integrating DEMATEL, ML, and ISM–MICMAC and offers practical strategies to strengthen SC security, foster trust in BC-based systems, and accelerate digital transformation in the construction industry.
Iqbal et al. (Sat,) studied this question.
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